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### What happened?
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1. **The LLM is returning a streaming wrapper instead of text**
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Because `streaming_on=True`, LiteLLM gives you a `CustomStreamWrapper` object.
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Your helper `parse_llm_output()` doesn’t know how to turn that object into a string, so the
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agent stores the wrapper itself.
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2. **`mcp_execution_flow()` received an empty string**
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When the agent tried to forward the tool‑call to your Fast‑MCP server it called
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`json.loads(<empty‑string>)`, which raised
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*“Expecting value: line 1 column 1 (char 0)”*.
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That means the HTTP call to `http://0.0.0.0:8000/mcp` returned **no body** –
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usually because the path is wrong or the server crashed.
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---
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### Fast MCP mock‑server: correct URL
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`FastMCP.run(transport="sse", port=8000)` serves SSE on
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`http://<host>:8000/stream` **and** accepts JSON‑RPC over POST on the root
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`http://<host>:8000/`.
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So point the client at the root (no `/mcp` suffix):
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```python
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math_server = MCPServerSseParams(
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url="http://0.0.0.0:8000", # <── just the origin
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headers={"Content-Type": "application/json"},
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timeout=5.0,
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sse_read_timeout=30.0,
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)
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```
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---
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### Stop streaming while you debug
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```python
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math_agent = Agent(
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agent_name="Math Agent",
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agent_description="Specialized agent for mathematical computations",
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system_prompt=MATH_AGENT_PROMPT,
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max_loops=1,
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mcp_servers=[math_server],
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streaming_on=False, # <── turn off for now
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)
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```
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(You can turn it back on when everything works and you extend
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`parse_llm_output()` to unwrap streams.)
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---
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### Make sure the LLM actually *produces* a tool call
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LLMs won’t invent a JSON‑RPC envelope unless you ask them.
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Add a very small system hint that mirrors the MCP schema, e.g.
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```python
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TOOL_CALL_INSTRUCTION = """
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When you want to use a math tool, reply with a JSON object only:
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{"tool_name": "<add|multiply|divide>", "a": <int>, "b": <int>}
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"""
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math_agent.short_memory.add(role="system", content=TOOL_CALL_INSTRUCTION)
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```
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Now “**add 2 and 4**” should yield
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```json
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{"tool_name":"add","a":2,"b":4}
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```
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which your `mcp_execution_flow()` can forward.
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---
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### Fix the empty‑body crash guard (optional)
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Inside `mcp_execution_flow()` (or `mcp_flow`) you can protect yourself:
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```python
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resp = client.post(...)
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if not resp.text:
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raise RuntimeError("MCP server returned empty body")
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```
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That turns an unhelpful JSON decode error into a clear MCP‑connection error.
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---
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### Quick test commands
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```bash
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# 1. Start mock server in one shell
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python mock_math_server.py # “Starting Mock Math Server on port 8000…”
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# 2. In another shell, make sure it answers
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curl -X POST http://0.0.0.0:8000 \
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-H "Content-Type: application/json" \
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-d '{"tool_name":"add","a":2,"b":4}'
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# → {"result":6}
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# 3. Run your client
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python mcp_client.py
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```
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If you now type `add 2 and 4`, the agent should respond **6** (with your
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explanation string) instead of the stream‑object dump.
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---
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#### Next steps
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* Re‑enable `streaming_on=True` and add handling of LiteLLM’s stream wrapper in
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`parse_llm_output()`.
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* Return the math result to the user in natural language (e.g. “2 + 4 = 6”).
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* Add error handling in the mock server (e.g. divide‑by‑zero already covered).
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That should give you a clean smoke‑test of **Fast MCP + Swarms Agent** without
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the function‑calling “craziness”. |
